Diagnose, Allocate, and Bind: Conditional Knowledge Injection into Language Models

Although language models can assimilate target knowledge through continued pre-training, conditional knowledge injection remains fundamentally limited by uniform token exposure and condition-independent recall shortcuts, where models often recall family-consistent values without binding them to their governing conditions. Under a fixed training-token budget, this exposes two coupled challenges: identifying facts requiring additional exposure and ensuring recalled values depend strictly on their queried conditions. This motivates a natural question: Can fact-level internal states be diagnosed to dynamically allocate exposure while explicitly binding values to their applicability conditions? To this end, we propose DAB (Diagnose, Allocate, and Bind), a framework linking fact-level memory diagnosis to adaptive token allocation and condition-dependent supervision. DAB exactly decomposes the gold-value negative log-likelihood into family-mass and within-family concentration terms, isolating under-supported facts from those confused with sibling values via behavior-calibrated routing. After a shared uniform stage, DAB reallocates a bounded token budget toward facts with diagnosed learning needs while strictly preserving minimum exposure for every fact. Furthermore, a late-stage Condition Necessity objective enforces a reference-relative margin between full-condition and condition-masked inputs, explicitly penalizing condition-independent shortcuts. Experiments on domain-specific technical standards (GB-Standard) and SQuAD-derived closed-book QA across Qwen3.5-4B-Base and Llama-3.1-8B-Instruct demonstrate the generality of DAB. On GB, DAB improves closed-book question-level accuracy by up to 8.23 percentage points over the same-renderer uniform baseline. On SQuAD, DAB reaches 64.06% with Qwen and 69.64% with Llama, outperforming the strongest baseline, Knowledge-Instruct (KI), by 6.22 and 19.50 percentage points, respectively. Factorial ablations confirm that diagnosis-guided allocation and conditional supervision provide complementary gains, framing conditional knowledge injection as the joint problem of allocating exposure and learning condition–value bindings.